It is known that high-throughput molecular profiling of biological samples has resulted in molecular signatures used to stratify the samples into particular categories.
This ranges from answering questions in the context of screening and diagnosis, to disease sub-typing and predicting response to treatment/therapy regiments. Many signatures are known within the art, in various stages of biological and clinical validation. Tests for predicting aggressiveness of breast cancer have for example been provided in commercial applications such as MammaPrint from Agendia or Oncotype DX from Genomic Health.
The fast development within the art has given rise to many molecular signatures that stratify patients into particular categories this data, is often incoherent and diverse, since no particular standard exists. The complex nature of biological systems, and the way these are studied, also makes it difficult to compare sets of genomic identities of different origin.
For example, if you have microarray-based assay that screens gene expression patterns significant for blood diseases, parts of these patterns may be the same as gene expression signatures significant for increased risk of stroke. However, even though parts of the signatures are similar, there is not disclosed within the art how to assess thematic overlap.
Hence, an improved method for analysis of biological data would be advantageous and in particular a method allowing for improved clinical decision support, increased flexibility, cost-effectiveness, speed and/or analytical precision would be advantageous.